Abstract

The International Price Program (IPP) collects data on United States trade with foreign nations and publishes monthly indexes on the import and export prices of U.S. merchandise and services. The IPP employs a three stage PPS design in which establishments, then broad product categories traded within establishments, and finally items within a category, are selected. Certainty selections can occur in the first two stages. We present three variations of the bootstrap rescaling method adapted to the IPP sample design: 1) sampling at the first stage, treating certainty units as probability units, 2) sampling that allows for certainties, and 3) a procedure that extends the previous method by collapsing single item strata. Finally, we compare the precision and bias of the three approaches by simulating 1000 samples of a simulated universe using the IPP sampling methodology.

The primary objective of Occupational Employment Statistics Survey, conducted by U.S. Bureau of Labor Statistics in partnership with the 50 States and District of Columbia, is to measure occupational employment and wages at the very detailed level of Metropolitan Statistical Areas (MSA) crossed by over 300 industries. That is, how many people are employed in one of the 800 Standard Occupational Codes (SOC) and what are the mean occupational wages for each industry by MSA. A given sampling frame contains about 175,000 non-empty MSA-by-industry cells. The occupational employment and wage estimates are also required at various aggregated levels of geography and industry. This study examines alternative sample allocation designs for a highly stratified population that deals with multiple issues such as establishment employment size and occupational diversity and variability.